Instructions to use mohammadalihumayun/trocr-ur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohammadalihumayun/trocr-ur with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="mohammadalihumayun/trocr-ur")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("mohammadalihumayun/trocr-ur")
model = AutoModelForImageTextToText.from_pretrained("mohammadalihumayun/trocr-ur")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mohammadalihumayun/trocr-ur with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohammadalihumayun/trocr-ur"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohammadalihumayun/trocr-ur",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mohammadalihumayun/trocr-ur

SGLang

How to use mohammadalihumayun/trocr-ur with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mohammadalihumayun/trocr-ur" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohammadalihumayun/trocr-ur",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mohammadalihumayun/trocr-ur" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohammadalihumayun/trocr-ur",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mohammadalihumayun/trocr-ur with Docker Model Runner:
```
docker model run hf.co/mohammadalihumayun/trocr-ur
```

trocr for Urdu

This model is a fine-tuned version of cxfajar197/urdu-ocr on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.0120
Cer: 0.2500

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.4637	0.3188	1000	2.3244	0.3040
0.4822	0.6376	2000	2.2832	0.3015
0.5518	0.9563	3000	2.0469	0.2796
0.4168	1.2751	4000	2.1507	0.2900
0.375	1.5939	5000	2.0784	0.2744
0.3911	1.9127	6000	2.0120	0.2500

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

Downloads last month: 1

Safetensors

Model size

0.3B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for mohammadalihumayun/trocr-ur

Base model

cxfajar197/urdu-ocr

Finetuned

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